MASTER CLASS

• Department: Executive Classes in HPM
• Instructor: Rebecca R Sale
  Info
• 3 points

GRAD SEM IN MOD JAPANESE LIT

Prerequisites: JPNS W4017-W4018 and the instructors permission. Selected works in modern Japanese fiction and criticism.

• Department: East Asian Languages and Cultures
• 4 points

Homer s Odyssey: Ruse and Narration

Homers Odyssey

A reading of Homer’s Odyssey with a focus on seminal episodes having to do with the
construction of the plot, and the intricate relationship between the Homeric narrator, his
characters, and internal and external audiences.

The Odyssey famously contains comparisons of its polytropos character (var.
reading polykrotos) to a poet, both explicitly (11.363ff.) and implicitly (19.203 with Hesiod,
Theogony 26-9). We will consider how the quality of being polytropos (including a tendency
towards ambiguity and indirection) factors into the ethics of narration in the poem, at every level
of the narrative. We will also consider the ethics of narration in the poem in relation to its
importance in the subsequent Greek rhetorical tradition. Archaic poetry, and the Homeric
poems, often suffer from the implicit bias associated with being the earliest extant Greek
literature, leading to the view that their content is naïve when compared against the literary
developments of the fifth century and the Hellenistic period. This seminar will approach
the Odyssey as a foundational text for Greek rhetorical culture, with particular attention to what it
offered the rhetorical culture of classical Athens.

• Department: Classics
• Instructor: Elizabeth K Irwin
  CULPA: 7 Info
• 3 points

Seneca Tragedy: Models, Motivations, Mea

This seminar aims to introduce students to the range and complexity of the tragedies composed by the eminent philosopher-politician, Lucius Annaeus Seneca (
c
. 2 BCE-65 CE). The course will combine intensive linguistic analysis of individual dramas with a focus on their political, philosophical and cultural meanings in the 1st century CE. Beyond our shared study of these highly allusive texts, a main goal will be to demonstrate that Seneca does not just write within a received tradition, but also uses remarkable artistic strategies by which to give new life to that tradition.

• Department: Classics
• Instructor: Gareth Williams
  CULPA: 7 Info
• 3 points

PRE-MODERN CHINESE FICTION

• Department: East Asian Languages and Cultures
• Instructor: Wei Shang
  CULPA: 4 Wikipedia h-index: 96 Info
• 4 points

Dissertation Research

Full time research for doctoral students.

• Department: Graduate School of Arts and Sciences
• Instructor: Richard Slusarczyk
  Info
• 12 points

CRITICAL ISSUES IN STAGE MANAGEMENT

CRIT ISSUES: CONFLICT RES

Conflict Resilience. Developing the comfort and skills necessary to respond to disagreements and mis-alignments is essential for leaders and stage managers. Through a series of discussions, experienced guests, reading, role-playing, and in-class exercises, this workshop style class will present an overview of Alternative Dispute Resolution (ADR) and Restorative Process theory and techniques with a practical focus on building our skills and comfort level to be able to reframe conflict as a chance for learning, understanding, and change.

Exceeding EDI. The impact of incorporating Equity, Diversity, Inclusion, Accessibility and Belonging into the commercial theater industry in a post George Floyd era. As stage managers, it is crucial that there is a framework for supporting the evolving identities and needs of the many populations present in a theater setting. Through a series of articles, in-class discussions, written reflections and conversations with working professionals, we will develop an understanding of a variety of social issues that currently exist in the industry while building a toolkit on how to navigate them.

• Department: Theatre Arts
• Instructor: Fredric Hanson
  Info
• 1.5 points

Valuation and Financial Statement Analys

The course material consists of detailed presentations, a comprehensive monograph—Earnings Quality, Fundamental Analysis and Valuation (available at https://ssrn.com/abstract=3794378), and many academic studies.

• Department: Accounting (ACCT)
• Instructor: Doron Nissim
  h-index: 24 Info
• 3 points

Navigating DEAIB: Strategies for Stage M

Diversity, Equity, Inclusion, Accessibility and Belonging (DEIAB) is more than a series of practices; it incorporates values and principles that run counter to the traditional, exclusionary power dynamics that have impacted the commercial theatre industry for decades. With a focus on creating or re-establishing positive relationships amongst all community members, Critical Issues in Stage Management considers real-world proficiencies in diversity, equity, inclusion and consent-forward practices that have direct application to our work as Stage Managers.

During this course we will examine the impact of incorporating Equity, Diversity, Inclusion, Accessibility and Belonging into the commercial theater industry in a post George Floyd era. As stage managers, it is crucial that there is a framework for supporting the evolving identities and needs of the many populations present in a theater setting. Through a series of articles, group projects, in-class discussions, written reflections and conversations with working professionals, we will develop an understanding of a variety of social issues that currently exist in the industry while building a toolkit on how to navigate them.

• Department: Theatre Arts
• Instructor: Erin Dunlevy
  Info
• 3 points

VISITING ARTISTS

• Department: Theatre Arts
• Instructor: Anne Bogart
  Wikipedia Info
• 0-3 points

QUANTUM FIELD THEORY II

Prerequisites: PHYS G6037-G6038. Relativistic quantum mechanics and quantum field theory.

• Department: Physics
• Instructor: Frederik Denef
  CULPA: 1 h-index: 36 Info
• 4.5 points

THEATRE PRACTICUM II

IMPULSE/STIMULUS/SPECIFICITY

TBD

• Department: Theatre Arts
• Instructor: Tina Benko
  Info
• 3 points

THEATRE PRACTICUM III

SELF TO PERFORMANCE

• Department: Theatre Arts
• 0-3 points

THEATRE PRACTICUM III

EARLY CAREER PREP

• Department: Theatre Arts
• Instructor: Scott V Whitehurst
  Info
• 0-3 points

PRODUCTION & TECHNICAL MANAGEMENT

PRODUCTION & TECH. MANAGE

• Department: Theatre Arts
• Instructor: Ryan Murphy
  Info
• 3 points

ADVANCED SCIENTIFIC COMPUTING

• Department: Physics
• Instructor: Robert D Mawhinney
  CULPA: 6 h-index: 49 Info
• 3 points

CONDENSED MATTER PHYSICS II

• Department: Physics
• Instructor: Yasutomo Uemura
  Info
• 3 points

Internship Class - Fall Session

This seminar is designed to provide an in-depth experiential learning experience concurrent with students’ public health or healthcare management internship. The seminar provides a supportive framework designed to enhance students’ professional and leadership experience by exploring common themes encountered in the fieldwork setting. The seminar will address the public health core competencies of leadership, communications, cultural competence, and professionalism.

The semester will begin with discussion of students’ project sites including project overview, goals for the field work experience and anticipated challenges. Focusing on professionalism in the workplace, students will assess how the internship aligns with their overarching learning goals. Students will also gain insights into successful leadership styles and skills through the design and implementation of an in-depth interview of a professional in their chosen field, a panel discussion of alumni in the field, and by developing and practicing oral and written communication and negotiation skills.

Throughout the course, students will develop a presentation that both demonstrates and reflects upon knowledge acquired through the internship experience. The semester culminates with students presenting the highlights of their project work.

This course is only open to students who either: (a) are required to complete a practicum as part of their degree, and have already completed their required practicum experience, and who have an internship (in a different setting or with different learning goals than their practicum) during the fall semester of their final year of school; OR (b) are enrolled in a Master’s program which does not require a practicum, and would like to take part in an optional internship during the fall semester of their final year of school."

Course pre-requisites: Completion of APeX (if required) and have an internship (in a different setting or with different learning goals than their practicum or APeX) during the semester.

Required permissions: This course is only open to students who have already completed their required practicum/APeX experience (if required), and who have an internship (in a different setting or with different learning goals than their practicum) during the fall semester. Students must submit a letter from their employer to join the waitlist for this course.

• Department: Public Health
• Instructor: Heather Krasna
  h-index: 5 Info
• 0.5 points

M.A. ESSAY DIRECTION

• Department: French
• 6 points

INDIV PROJECTS IN MUSICOLOGY

INDIV PROJECT IN MUSICOLOGY

Sec. 1: Ethnomusicology; Sec. 2: Historical Musicology; Sec. 3: Music Theory; Sec. 4: Music Cognition; Sec. 5: Music Philosophy.

• Department: Music
• Instructor: Susan Boynton
  CULPA: 18 Info
• Instructor: Alessandra M Ciucci
  CULPA: 1 Info
• Instructor: Joseph P Dubiel
  CULPA: 3 Info
• Instructor: Kevin Fellezs
  CULPA: 4 Info
• Instructor: Aaron A Fox
  CULPA: 7 Info
• Instructor: Giuseppe Gerbino
  CULPA: 12 Info
• Instructor: Christopher J Washburne
  CULPA: 31 Info
• 3 points

INDIV PROJECTS IN MUSICOLOGY

Sec. 1: Ethnomusicology; Sec. 2: Historical Musicology; Sec. 3: Music Theory; Sec. 4: Music Cognition; Sec. 5: Music Philosophy.

• Department: Music
• Instructor: Ruth Opara
  Info
• 3 points

Applied Regression I

APPLIED REGRESSION I

This course will provide an introduction to the basics of regression analysis. The class will proceed systematically from the examination of the distributional qualities of the measures of interest, to assessing the appropriateness of the assumption of linearity, to issues related to variable inclusion, model fit, interpretation, and regression diagnostics. We will primarily use scalar notation (i.e. we will use limited matrix notation, and will only briefly present the use of matrix algebra).

• Department: Biostatistics
• 3 points

Healthcare Management, Design, and Strat

Healthcare Mgmt Design &

COURSE DESCRIPTION AND LEARNING OBJECTIVES
The U.S. healthcare system is an enormously complex, trillion-dollar industry, accounting for approximately 18% of GDP. The healthcare sector is vast and covers multiple different players from patients, providers, payors, to bio/pharma developers and innovators. Each part of the healthcare sector brings a different set of business challenges that touch on aspects from Finance, Marketing, Operations, Accounting, and more. The healthcare industry is going through a transformation with the development of new technologies, increased sophistication and adoption of electronic medical records systems and data collection architectures, and new models of the delivery of care and payment systems. This tremendous dynamism is unmatched by any other industry and offers incredible opportunities for new business endeavors. This course provides students the opportunity to learn about i) approaches to doing consulting; ii) key considerations diving strategic decision-making in the healthcare industry; and iii) the chance to put these concepts to practice by working on a set of company-sponsored applied projects. Student teams of 5-6 people, with 3-4 MBA (CBS) students and 1-2 medical (CUIMC) students, will work hand in hand with the instructors and company representatives to achieve company goals through the practical application of fundamental core business practices. Through these projects, students will be exposed to the unique challenges and opportunities in the healthcare sector. Some examples of potential projects include:

For a pharmaceutical company, evaluate the commercial potential of a new therapeutic class.
Evaluate and identify improvement opportunities in the patient evaluation process of a clinical unit at CUIMC. Redesign the standard workflow ad evaluate the financial and operational impact of these changes.
Utilize consumer predictive analytics to guide marketing strategies for a biotech device.
The scope of sponsoring companies spans large firms in biotech and pharmaceuticals, smaller startups in healthcare analytics and/or biotech, large provider systems, as well as smaller clinics. Companies provide the project scope and relevant data, faculty provides guidance on best practices, and your team will provide the answers.

Throughout this course, students will execute on a healthcare project to:
Use tools and ideas from operations, business analytics, finance, marketing, and strategy to solve interesting and exciting business proble

• Department: Decision, Risk and Operations
• 3 points

Applied Regression

This course will provide students with a thorough introduction to applied regression analysis, which has been a commonly used and almost standard method for analyzing continuous response data in Public Health research. Topics covered include simple linear regression, multiple linear regression, analysis of variance, parameter estimation, hypothesis testing, interpretation of estimates, interaction terms, variable recoding, examination of validity of underlying assumptions, regression diagnostics, model selection, logistic regression analysis, generalized linear models as well as discussions on relationships of variables in research and using regression results for either prediction or estimation purposes. Real data are emphasized and analyzed using SAS.

• Department: General Public Health
• Instructor: Dan Liao
  Info
• 3 points

ADVANCED TOPICS IN IEOR

Diffusion Models AI & RL

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• Department: Industrial Engineering and Operations Research
• Instructor: Xunyu Zhou
  h-index: 54 Info
• 3 points

ADVANCED TOPICS IN IEOR

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• Department: Industrial Engineering and Operations Research
• Instructor: Clifford Stein
  CULPA: 31 Wikipedia h-index: 53 Info
• Instructor: Yuri Faenza
  h-index: 11 Info
• Instructor: Jay Sethuraman
  CULPA: 10 Info
• Instructor: Antonius B Dieker
  CULPA: 10 h-index: 19 Info
• 3 points

VISUAL ART LAB

• Department: Visual Arts
• Instructor: Rirkrit Tiravanija
  Wikipedia Info
• 3 points

Business Analytics I (Full Term)

This course extends and deepens the material you learned in business analytics. We will apply these methods in more unstructured and diverse situations, introduce new analytics tools and methods (including Tableau Visualization, text mining, and random forests), and study a modern framework for overfitting reduction called regularization that underlies much of modern machine learning. This course does not require coding or knowledge beyond Business Analytics, but the mathematical sophistication level will be somewhat more advanced.

• Department: Decision, Risk and Operations
• Instructor: Charles R Guetta
  Info
• 3 points

Modern Middle East: Themes in Current Re

This course is open to Ph.D. students and advanced M.A. students conducting research on
aspects of the modern, culture, politics, and history of the Middle East and adjacent regions. Its
temporal focus is the three centuries from roughly the mid-eighteenth to the mid-twentieth
century, but those whose research deals with other periods are welcome to participate.
The course has three aims. The first is to provide an opportunity to read and engage with some
of the more recent scholarship in the field, especially work published in the last ten years,
organized around several current academic debates. The second is to provide a seminar in
which those preparing a master’s paper, M.Phil. examination list, or Ph.D. prospectus, or a term
paper intended for conference presentation or publication, can develop and present a draft of
their work. We will choose readings to accompany each paper, focusing on recent scholarship
that informs or extends the issues addressed in the research. The course will enable students to
clarify and test the questions that shape their work and better situate them within current
scholarship. The third aim is to train students in the art of framing questions and shaping
debate for an advanced, reading-intensive graduate-level seminar.
The course is intended primarily for MESAAS students. Those from other departments are
welcome but require the permission of the instructor to enroll.

• Department: Middle Eastern, South Asian and African Studies
• Instructor: Timothy Mitchell
  Wikipedia h-index: 34 Info
• 4 points

Patient Oriented Research Career Develop

POR CAREER DEVELOPMENT CO

The main objective of this course is to provide Columbia University's Clinical & Translational Science award trainees, students, and scholars with skills and knowledge that will optimize their chances of entering into a satisfying academic career. The course will emphasize several methodological and practical issues related to the development of a science career. The course will also offer support and incentives by facilitating timely use of CTSA resources, obtaining expert reviews on writing and curriculum vitae, and providing knowledge and resources for the successful achievement of career goals.

• Department: Biostatistics
• Instructor: Marisa N Spann
  Info
• 0.5 points

Business Analytics II: Foundations of AI

Business Analytics II: AI

Business analytics refers to the ways in which enterprises such as businesses, non-profits, and governments use data to gain insights and make better decisions. Business analytics is applied in operations, marketing, finance, and strategic planning among other functions. Modern data collection methods – arising in bioinformatics, mobile platforms, and previously unanalyzable data like text and images – are leading an explosive growth in the volume of data available for decision making. The ability to use data effectively to drive rapid, precise, and profitable decisions has been a critical strategic advantage for companies as diverse as Walmart, Google, Capital One, and Disney. Many startups are based on the application of AI & analytics to large databases. With the increasing availability of broad and deep sources of information – so-called “Big Data” – business analytics are becoming an even more critical capability for enterprises of all types and all sizes. AI is beginning to impact every dimension of business and society. In many industries, you will need to be literate in AI to be a successful business leader. The Business Analytics sequence is designed to prepare you to play an active role in shaping the future of AI and business. You will develop a critical understanding of modern analytics methodology, studying its foundations, potential applications, and – perhaps most importantly – limitations.

• Department: Decision, Risk and Operations
• Instructor: Charles R Guetta
  Info
• 1.5 points

Business Analytics II: Foundations of AI

Business Analytics II: AI

Business analytics refers to the ways in which enterprises such as businesses, non-profits, and governments use data to gain insights and make better decisions. Business analytics is applied in operations, marketing, finance, and strategic planning among other functions. Modern data collection methods – arising in bioinformatics, mobile platforms, and previously unanalyzable data like text and images – are leading an explosive growth in the volume of data available for decision making. The ability to use data effectively to drive rapid, precise, and profitable decisions has been a critical strategic advantage for companies as diverse as Walmart, Google, Capital One, and Disney. Many startups are based on the application of AI & analytics to large databases. With the increasing availability of broad and deep sources of information – so-called “Big Data” – business analytics are becoming an even more critical capability for enterprises of all types and all sizes. AI is beginning to impact every dimension of business and society. In many industries, you will need to be literate in AI to be a successful business leader. The Business Analytics sequence is designed to prepare you to play an active role in shaping the future of AI and business. You will develop a critical understanding of modern analytics methodology, studying its foundations, potential applications, and – perhaps most importantly – limitations.

• Department: Decision, Risk and Operations
• Instructor: Charles R Guetta
  Info
• 1.5 points

Probability

PROBABILITY

The course aims to present the fundamental principles behind probability theory and lay the foundations for various kinds of statistical/biostatistical courses such as statistical inference, multivariate analysis, regression analysis, clinical trials, asymptotics, and so on. Students will learn how to implement probability methods in various types of applications.

• Department: Biostatistics
• Instructor: Zhezhen Jin
  h-index: 51 Info
• 3 points

Data Science I

Contemporary biostatistics and data analysis depends on the mastery of tools for computation, visualization, dissemination, and reproducibility in addition to proficiency in traditional statistical techniques. The goal of this course is to provide training in the elements of a complete pipeline for data analysis. It is targeted to MS, MPH, and PhD students with some data analysis experience.

• Department: Biostatistics
• Instructor: Arthur Jeff Goldsmith
  Info
• 3 points

SEM-HISTROCL MUSICLGY-BAROQUE

• Department: Music
• Instructor: Giuseppe Gerbino
  CULPA: 12 Info
• 3 points

Operations Strategy

A firm's operations encompass all the activities that are performed in order to produce and deliver a product or a service. An operations strategy refers to a set of operational decisions that a firm makes to achieve a long-term competitive advantage. These decisions may be about the firms facilities, its technology/process choices, its relationships with both upstream and downstream business partners etc. The goal of this course is to provide students with an understanding of how and why operational decisions are integral to a firms success. The course builds on concepts from the core Operations Management course and the core Strategy Formulation course. It is highly relevant to anyone whose work requires the strategic analysis of a firms operations, including those interested in consulting, entrepreneurship, mergers and acquisitions, private equity, investment analysis, and general management. The course consists of four modules. The first module, Strategic Alignment," explores the question of how a firms operations should be structured so as to be consistent with the firms chosen way to compete. The second module, "Firm Boundaries," considers the question of what operational activities should remain in house and what should be done by a business partner and the long-term implications of these decisions on competitive advantage. This module also addresses the issue of managing the business relationships with supply chain partners. The third module, "Internal Operations," considers key decision categories in operations, e.g., capacity decisions, process choices, IT implementation, and managing networks, and shows how these decisions can lead to distinctive capabilities. The final module, "New Challenges," is set aside to address new topics that reflect the current trends in the business environment."

• Department: Decision, Risk and Operations
• Instructor: Medini Singh
  Info
• 3 points

Operations Strategy

A firm's operations encompass all the activities that are performed in order to produce and deliver a product or a service. An operations strategy refers to a set of operational decisions that a firm makes to achieve a long-term competitive advantage. These decisions may be about the firms facilities, its technology/process choices, its relationships with both upstream and downstream business partners etc. The goal of this course is to provide students with an understanding of how and why operational decisions are integral to a firms success. The course builds on concepts from the core Operations Management course and the core Strategy Formulation course. It is highly relevant to anyone whose work requires the strategic analysis of a firms operations, including those interested in consulting, entrepreneurship, mergers and acquisitions, private equity, investment analysis, and general management. The course consists of four modules. The first module, Strategic Alignment," explores the question of how a firms operations should be structured so as to be consistent with the firms chosen way to compete. The second module, "Firm Boundaries," considers the question of what operational activities should remain in house and what should be done by a business partner and the long-term implications of these decisions on competitive advantage. This module also addresses the issue of managing the business relationships with supply chain partners. The third module, "Internal Operations," considers key decision categories in operations, e.g., capacity decisions, process choices, IT implementation, and managing networks, and shows how these decisions can lead to distinctive capabilities. The final module, "New Challenges," is set aside to address new topics that reflect the current trends in the business environment."

• Department: Decision, Risk and Operations
• Instructor: Medini Singh
  Info
• 3 points

Introduction to Mathematical Statistics

Intro Mathematical Statis

The first portion of this course provides an introductory-level mathematical treatment of the fundamental principles of probability theory, providing the foundations for statistical inference.  Students will learn how to apply these principles to solve a range of applications.  The second portion of this course provides a mathematical treatment of (a) point estimation, including evaluation of estimators and methods of estimation; (b) interval estimation; and (c) hypothesis testing, including power calculations and likelihood ratio testing.

• Department: Biostatistics
• Instructor: Todd Ogden
  Info
• 3 points

Service Operations Management

This course examines both traditional and new approaches for achieving operational competitiveness in service businesses. Major service sectors such as health care, repair / technical support services, banking and financial services, transportation, restaurants, hotels and resorts are examined. The course addresses strategic analysis and operational decision making, with emphasis on the latter. Its content also reflects results of a joint research project with the consulting firm Booz Allen Hamilton, which was initiated in 1996 to investigate next-generation service operations strategy and practices. Topics include the service concept and operations strategy, the design of effective service delivery systems, productivity and quality management, response time (queueing) analysis, capacity planning, yield management and the impact of information technology. This seminar is intended for students interested in consulting, entrepreneurship, venture capital or general management careers that will involve significant analysis of a service firms operations.

• Department: Decision, Risk and Operations
• Instructor: Medini Singh
  Info
• 3 points

Service Operations Management

This course examines both traditional and new approaches for achieving operational competitiveness in service businesses. Major service sectors such as health care, repair / technical support services, banking and financial services, transportation, restaurants, hotels and resorts are examined. The course addresses strategic analysis and operational decision making, with emphasis on the latter. Its content also reflects results of a joint research project with the consulting firm Booz Allen Hamilton, which was initiated in 1996 to investigate next-generation service operations strategy and practices. Topics include the service concept and operations strategy, the design of effective service delivery systems, productivity and quality management, response time (queueing) analysis, capacity planning, yield management and the impact of information technology. This seminar is intended for students interested in consulting, entrepreneurship, venture capital or general management careers that will involve significant analysis of a service firms operations.

• Department: Decision, Risk and Operations
• Instructor: Medini Singh
  Info
• 3 points

Service Operations Management

This course examines both traditional and new approaches for achieving operational competitiveness in service businesses. Major service sectors such as health care, repair / technical support services, banking and financial services, transportation, restaurants, hotels and resorts are examined. The course addresses strategic analysis and operational decision making, with emphasis on the latter. Its content also reflects results of a joint research project with the consulting firm Booz Allen Hamilton, which was initiated in 1996 to investigate next-generation service operations strategy and practices. Topics include the service concept and operations strategy, the design of effective service delivery systems, productivity and quality management, response time (queueing) analysis, capacity planning, yield management and the impact of information technology. This seminar is intended for students interested in consulting, entrepreneurship, venture capital or general management careers that will involve significant analysis of a service firms operations.

• Department: Decision, Risk and Operations
• Instructor: Medini Singh
  Info
• 3 points

Survival Analysis

SURVIVAL ANALYSIS

This course focuses on methods for the analysis of survival data, or time-to-event data. Survival analysis is a method for analyzing survival data or failure (death) time data, that is time-to-event data, which arises in a number of applied fields, such as medicine, biology, public health, epidemiology, engineering, economics, and demography. A special course of difficulty in the analysis of survival data is the possibility that some individual may not be observed for the full time to failure. Instead of knowing the failure time t, all we know about these individuals is that their time-to-failure exceeds some value y where y is the follow-up time of these individuals in the study. Students in this class will learn how to make inference for the event times with censored. Topics to be covered include survivor functions and hazard rates, parametric inference, life-table analysis, the Kaplan-Meier estimator, k-sample nonparametric test for the equality of survivor distributions, the proportional hazards regression model, analysis of competing risks and bivariate failure-time data.

• Department: Biostatistics
• 3 points

GROUP THEORY

• Department: Chemistry
• Instructor: Jack R Norton
  CULPA: 6 Wikipedia Info
• 2.5 points

Supply Chain Management

Supply chain management entails managing the flow of goods and information through a production or distribution network to ensure that the right goods are delivered to the right place in the right quantity at the right time. Two primary objectives are to gain competitive edge via superior customer service and to reduce costs through efficient procurement, production and delivery systems. Supply chain management encompasses a wide range of activities — from strategic activities, such as capacity expansion or consolidation, make/buy decisions and initiation of supplier contracts, to tactical activities, such as production, procurement and logistics planning, to, finally, operational activities, such as operations scheduling and release decisions, batch sizing and issuing of purchase orders.

• Department: Decision, Risk and Operations
• Instructor: Nicole Dehoratius
  Info
• 1.5 points

Supply Chain Management

Supply chain management entails managing the flow of goods and information through a production or distribution network to ensure that the right goods are delivered to the right place in the right quantity at the right time. Two primary objectives are to gain competitive edge via superior customer service and to reduce costs through efficient procurement, production and delivery systems. Supply chain management encompasses a wide range of activities — from strategic activities, such as capacity expansion or consolidation, make/buy decisions and initiation of supplier contracts, to tactical activities, such as production, procurement and logistics planning, to, finally, operational activities, such as operations scheduling and release decisions, batch sizing and issuing of purchase orders.

• Department: Decision, Risk and Operations
• Instructor: Nicole Dehoratius
  Info
• 1.5 points

Applied Regression II

APPLIED REGRESSION II

This course will introduce the statistical methods for analyzing censored data, non-normally distributed response data, and repeated measurements data that are commonly encountered in medical and public health research. Topics include estimation and comparison of survival curves, regression models for survival data, logit models, log-linear models, and generalized estimating equations. Examples are drawn from the health sciences.

• Department: Biostatistics
• Instructor: Xin Ma
  Info
• 3 points

TV PILOT: COMEDY & DRAMA

With the pilot as a focal point, this course explores the opportunities and challenges of telling and sustaining a serialized story over a protracted period of time with an emphasis on the creation, borne out of character, of the quintessential premise and the ongoing conflict, be it thematic or literal, behind a successful series.

Early in the semester, students may be required to present/pitch their series idea. During the subsequent weeks, students will learn the process of pitching, outlining, and writing a television pilot, that may include story breaking, beat-sheets or story outline, full outlines, and the execution of either a thirty-minute or hour-long teleplay. This seminar may include reading pages and giving notes based on the instructor but may also solely focus on the individual process of the writer.  


Students may only enroll in one TV Writing workshop per semester.

• Department: Film
• Instructor: David Klass
  Info
• Instructor: Blair Singer
  Info
• Instructor: Carter Bays
  Info
• Instructor: Blair Singer
  Info
• 3 points

SCENE STUDY

SCENE STUDY: THE GREEKS

• Department: Theatre Arts
• Instructor: Brian Kulick
  Info
• 3 points

Commercial Society

Before capitalism, there was commercial society. This course examines European debates about commerce, luxury, and social organization from the late seventeenth through the late eighteenth centuries. We will survey a range of theoretical perspectives on the new forms of commercial sociability and political life emerging in Europe, whether triumphant, despairing, or ambivalent.

• Department: Political Science
• Instructor: Daniel Luban
  Info
• 4 points

Applied Regression Analysis

The goal of this course is to provide students with practical experience in building and analyzing regression models to address business problems.
The course picks up where the core course in Managerial Statistics left off. We will begin with a brief review of regression analysis as covered in the core and then move on to new topics, including model selection, interaction effects, nonlinear effects, classification problems, and forecasting.
All material will be covered through examples, exercises, and cases. In addition, students will work in groups on a final project of their choosing. The goal of the project is to address a specific business problem through statistical analysis.

• Department: Decision, Risk and Operations
• Instructor: Paul Glasserman
  Info
• 1.5 points

Leading Sustainable Operations

The importance of designing, building, and leading sustainable organizations is indisputable. Sustainability encompasses not only the environmental footprint of an organization but also the way in which firms treat workers and customers both within their firm and supply chain network. Understanding the role of operational excellence and strategic supply chain management in achieving sustainability is critical for effective leadership.

This course examines a variety of approaches to designing sustainability into an organization’s operations and how to measure and reduce a firm’s operational environmental impact. We also explore themes of risk, accountability, and sustainability within global supply chains. What challenges do firms face in being socially responsible when managing globally distributed supply chains? Three themes comprise this course: (1) designing sustainable operations, (2) drivers and consequences of sustainability, and (3) global sourcing and social responsibility.

• Designing Sustainable Operations. Sample cases include – REI Rentals, All Birds, IndigoAg, Supply Chain Hubs in Humanitarian Logistics.
• Drivers and Consequences of Sustainability. Sample cases include – Fiji Water, Aspen Ski Company.
• Global Sourcing & Social Responsibility. Sample cases include – IKEA, Ready Made Garment Industry, Roche & Tamiflu.

• Department: Decision, Risk and Operations
• Instructor: Nicole Dehoratius
  Info
• 1.5 points

Design of Medical Experiments

DESIGN OF MEDICAL EXPERIM

This course covers the fundamental principles and techniques of experimental designs in clinical studies. This is a required course for MS, DrPH and Ph.D. in Biostatistics. Topics include reliability of measurement, linear regression analysis, parallel groups design, analysis of variance (ANOVA), multiple comparison, blocking, stratification, analysis of covariance (ANCOVA), repeated measures studies; Latin squares design, crossover study, randomized incomplete block design, and factorial design.

• Department: Biostatistics
• Instructor: Helen Li
  Info
• 3 points

Business Analytics III: Modern AI, Deep

Business Analytics III: A

Business analytics refers to the ways in which enterprises such as businesses, non-profits, and
governments use data to gain insights and make better decisions. Business analytics is applied
in operations, marketing, finance, and strategic planning among other functions. Modern data
collection methods – arising in bioinformatics, mobile platforms, and previously unanalyzable
data like text and images – are leading an explosive growth in the volume of data available for
decision making. The ability to use data effectively to drive rapid, precise, and profitable
decisions has been a critical strategic advantage for companies as diverse as Walmart, Google,
Capital One, and Disney. Many startups are based on the application of AI & analytics to large
databases. With the increasing availability of broad and deep sources of information – so-called
“Big Data” – business analytics are becoming an even more critical capability for enterprises of
all types and all sizes.

• Department: Decision, Risk and Operations
• Instructor: Charles R Guetta
  Info
• 1.5 points

Business Analytics III: Modern AI, Deep

Business Analytics III: A

Business analytics refers to the ways in which enterprises such as businesses, non-profits, and
governments use data to gain insights and make better decisions. Business analytics is applied
in operations, marketing, finance, and strategic planning among other functions. Modern data
collection methods – arising in bioinformatics, mobile platforms, and previously unanalyzable
data like text and images – are leading an explosive growth in the volume of data available for
decision making. The ability to use data effectively to drive rapid, precise, and profitable
decisions has been a critical strategic advantage for companies as diverse as Walmart, Google,
Capital One, and Disney. Many startups are based on the application of AI & analytics to large
databases. With the increasing availability of broad and deep sources of information – so-called
“Big Data” – business analytics are becoming an even more critical capability for enterprises of
all types and all sizes.

• Department: Decision, Risk and Operations
• Instructor: Charles R Guetta
  Info
• 1.5 points

Transforming the Delivery of H

This course provides an advanced, critical analysis of the delivery and payment of healthcare services in the U.S. It will analyze the attractiveness and feasibility of new approaches to address the challenges facing providers, payors and patients operating in an inefficient, misaligned, and fragmented healthcare system. Particular focus will be given to the impact of the 2009 HITECH Act as well as the Affordable Care Act (ACA) of 2010. There will be guest lectures by some of a variety of major leaders in healthcare business and policy. Students with limited knowledge of healthcare payment and delivery systems are unlikely to benefit from the course given the advanced nature of the material that will be covered."

• Department: Decision, Risk and Operations
• Instructor: Brian Regan
  Info
• 1.5 points

Analysis of Categorical Data

ANALYSIS OF CATEGORICAL D

A comprehensive overview of methods of analysis for binary and other discrete response data, with applications to epidemiological and clinical studies. It is a second level course that presumes some knowledge of applied statistics and epidemiology. Topics discussed include 2 × 2 tables, m × 2 tables, tests of independence, measures of association, power and sample size determination, stratification and matching in design and analysis, interrater agreement, logistic regression analysis.

• Department: Biostatistics
• 3 points

ANALYSIS OF CATEGORICAL DATA

• Department: ONLINE MS IN EPIDEMIOLOGY
• Instructor: Tonguc Yaman
  Info
• 3 points

Humans, Animals and Machines

Political theory in the 21st century must address the role of non-humans and examine the traditional priority of human beings.  What rights, if any, should animals and AI have and on what basis?  Can we even say what it means to be distinctively human?  Can that distinctiveness support consequential moral or political conclusions?  Is there a clear basis for human or non-human equality which can ground rights or democratic institutions?  Or must we find a post-humanist politics of some kind?

• Department: Political Science
• 4 points

DIALECTS & ACCENTS

This course continues the actor’s work of experiencing voice and text in a free body as a means to develop versatile and transformative speech.  Students will deepen and refine their knowledge of the phonemes of the International Phonetic Alphabet (IPA), as well as the ability to categorize and utilize Lexical Sets in pursuit of a dialect/accent.  Students will demonstrate their ability to notate texts and transcribe dialects and accents into both IPA and practically apply the framework of the Four Pillars and the Voice Recipe.

The student will use these tools, supplemented by handouts, video & audio resources and independent research, to study several accents/dialects in class as well as at least one additional independently researched accent/dialect. The goal of the class is to expand upon the actor’s choices of speech and vocal expression and to acquaint her/him with the resources necessary to truthfully portray an individual utilizing a dialect/accent on stage or screen.

Students will develop their own unique process for learning accents and dialects
, as well as efficiently and effectively applying their progression to texts via a combination of practice sentences, scene work, conversation, improvisation, cold readings, and a prepared monologue.  Students will complete the course having created a personal, in-depth method for researching and performing a role in which an accent or dialect is required.

Students will do self-directed and supported research as part of their study.  They will consciously and intelligently assimilate this contextual research into their embodiment choices.  The final project is a presentation of their research and the sharing of a monologue that is ideally
written in the student’s selected dialect or accent
.

• Department: Theatre Arts
• Instructor: Elizabeth Hayes
  Info
• 2 points

Statistical Methods for Causal Inference

Stat Mthds for Causal Inf

Substantive questions in empirical scientific and policy research are often causal. This class will introduce students to both statistical theory and practice of causal inference. As theoretical frameworks, we will discuss potential outcomes, causal graphs, randomization and model-based inference, causal mediation, and sufficient component causes. We will cover various methodological tools including randomized experiments, matching, inverse probability weighting, instrumental variable approaches, dynamic causal models, sensitivity analysis, statistical methods for mediation and interaction. We will analyze the strengths and weaknesses of these methods. The course will draw upon examples from social sciences, public health, and other disciplines. The instructor will illustrate application of the approaches using R/SAS/STATA software. Students will be evaluated and will deepen the understanding of the statistical principles underlying the approaches as well as their application in homework assignments, a take home midterm, and final take home practicum.

• Department: Biostatistics
• Instructor: Linda Valeri
  h-index: 24 Info
• 3 points

Intro to Biology Course (T3ST)

Test Course for Vergil Launch Demonstration

• Department: OFFC OF THE REGISTRAR
• Instructor: Faculty
  h-index: 37 Info
• 2 points

Graphical Models for Complex Health Data

GRAPHICAL MODELS COMPLEX

This is a course at the intersection of statistics and machine learning, focusing on graphical models. In complex systems with many (perhaps hundreds or thousands) of variables, the formalism of graphical models can make representation more compact, inference more tractable, and intelligent data-driven decision-making more feasible. We will focus on representational schemes based on directed and undirected graphical models and discuss statistical inference, prediction, and structure learning. We will emphasize applications of graph-based methods in areas relevant to health: genetics, neuroscience, epidemiology, image analysis, clinical support systems, and more. We will draw connections in lecture between theory and these application areas. The final project will be entirely “hands on,” where students will apply techniques discussed in class to real data and write up the results.

• Department: Biostatistics
• Instructor: Daniel S Malinsky
  Info
• 3 points

DIGITAL LITERACY-DECISION MKRS

COURSE DESCRIPTION
Unrelenting technological progress demands entrepreneurs, executives, and managers to continually upgrade their skills in the pursuit of emerging opportunities. As “software eats the world”, executives from all industries are increasingly called upon to be “Full Stack”: capable of making competent decisions across domains as diverse as digital technology, design, product, and marketing.
In this course, we begin with primers on code, design, and product management. Once the foundation is laid, we examine the best practices for building great products and exceptional teams. We conclude with an overview of how technology is changing the way products are marketed, distributed, and monetized. Our goal is to equip “non-technical” executives with the terminology, tools, and context required to effect change in a software­ and internet-driven world.
COURSE LEARNING OBJECTIVES
To provide an understanding of the technologies that we encounter everyday, and how history can inform the technology decisions executives face today.
To become familiar the concepts that underpin modern computer programming, empowering managers to engage with engineers credibly and confidently.
To shed light on the processes and tools designers use to solve user-facing design and architecture challenges.
To clarify what product managers do, walk through the nitty-gritty of managing software development, and equip executives with the best practices for evaluating and improving their products.
To prepare managers to identify, recruit, and nurture the technical talent they will need to succeed in today’s highly competitive labor market.
To familiarize students with the dynamic context in which technology products live, ensuring the profitable and widespread delivery of those products.

• Department: Decision, Risk and Operations
• Instructor: Marco A Morales Barba
  Info
• 1.5 points

Biostatistical Methods I

BIOSTATISTICAL METHODS I

This one-semester course introduces basic applied descriptive and inferential statistics. The first part of the course includes elementary probability theory, an introduction to statistical distributions, principles of estimation and hypothesis testing, methods for comparison of discrete and continuous data including chi-squared test of independence, t-test, analysis of variance (ANOVA), and their non-parametric equivalents. The second part of the course focuses on linear models (regression) theory and their practical implementation.

• Department: Biostatistics
• 3 points

ACTING III

MFA acting students will tackle verse drama and heightened language. We will spend much of our time investigating Shakespeare’s writing, with a focus on King Lear and Much Ado about Nothing, and will weave in contemporary heightened language texts throughout the semester. 

Goals



To develop students into keen interpreters of heightened theatrical language, both classical and contemporary



To enable students to express their instinctive emotional responses to the rhythms, sounds and the mysteries contained in great language texts



To bring character and the specific imaginative world of each play alive thru the language



To foster each actor’s unique voice

• Department: Theatre Arts
• Instructor: Peter Fernandez
  Wikipedia Info
• 3 points

Sports Analytics

Sports analytics refers to the use of data and quantitative methods to measure performance and make decisions to gain advantage in the competitive sports arena. This course builds on the Business Analytics core course and is designed to help students to develop and apply analytical skills that are useful in business, using sports as the application area. These skills include critical thinking, mathematical modeling, statistical analysis, predictive analytics, game theory, optimization and simulation. These skills will be applied to sports in this course, but are equally useful in many areas of business.There will be three main topics in the course: (1) measuring and predicting player and team performance, (2) decision-making and strategy in sports, and (3) fantasy sports and sports betting. Typical questions addressed in sports analytics include: How to rank players or teams? How to predict future performance of players or teams? How much is a player on a team worth? How likely are extreme performances, i.e., streaks? Are there hot-hands in sports performances? Which decision is more likely to lead to a win (e.g., attempt a stolen base or not in baseball, punt or go for it on fourth down in football, dump and chase or not in hockey, pull the goalie or not in hockey)? How to form lineups in daily fantasy sports? How to manage money in sports betting? How to analyze various ``prop'' bets?The main sports discussed in the course will be baseball, football, basketball, hockey, and golf. Soccer, tennis, and other sports will be briefly discussed.

Students are welcome to pursue any sport in more detail (e.g., cricket, rugby, auto racing, horse racing, Australian rules football, skiiing, track and field, or even card games such as blackjack, poker, etc.) in a project. Class sessions will involve a mixture of current events, lecture, discussion, and hands-on analysis with computers in class. Each session will typically address a question from a sport using an important analytical idea (e.g., mean reversion) together with a mathematical technique (e.g., regression). Because of the "laboratory" nature of part of the sessions, students should bring their laptops to each class.

• Department: Decision, Risk and Operations
• Instructor: Mark N Broadie
  Info
• 3 points

Digital Health Startups

Healthcare represents 18% of the U.S. economy, yet it is one of the last sectors to undergo technology-based transformation. Digital health represents the convergence of healthcare and technology, with the aim to improve access to care, reduce inefficiencies in healthcare delivery, lower costs, enhance the quality of patient care, make treatments more targeted and personalized, and empower consumers to better manage their own health and well-being.
In recent years there has been an explosion of new digital health startups focused on these key objectives. Digital health has become the bellwether of venture funding, outgrowing both traditional healthcare and technology sectors. Venture funding in this category exceeded $29 billion in 2021, double the previous year and a 2,325% increase from 2011.
This course will analyze the unique characteristics and strategies of digital health companies as students form groups to act as venture capitalists and develop investment memos for real companies that are pitched by their founders. Past companies that have been pitched in this course— Maven Clinic, Grand Rounds (Included Health), and Simple Health— have gone on to become high-growth, billion-dollar companies.
Students will analyze key objectives of new businesses and determinants of success including unit economics, product differentiation, go-to market strategies, customer acquisition, marketing tactics, scale-up/growth opportunities, and other business optimization approaches. The course will allow students to hone their investment skills including questions to ask during an entrepreneur’s pitch, developing an investment thesis, and how to structure and write an investment memo. This course will address these issues through a mixture of lectures, case studies, and guest speakers (entrepreneurs and investors) from the digital health sector.

• Department: Decision, Risk and Operations
• Instructor: Halle Tecco
  Wikipedia Info
• 1.5 points

Intro to Databases for Busines

We don’t think about databases much, right? At least not when they’re working right. But they’re all around us. They’re in every product we use. And when they don’t work (think about the iCloud, LinkedIn, or Ashley Madison data breaches in which hundreds of millions of emails and passwords were exposed) the consequences can be extreme.
Every modern company stores their data in a database (it’s like a really big version of Excel), and if you want to analyze the data, you may be expected to know how to access it yourself. In fact, at many companies are requiring even their business leaders to have an understanding of databases. At the very least, knowing how to set up and interact with databases will improve your ability to GSD (get stuff done), strengthen your understanding of how technology works, and make you less of a pain for developers to work with.
In this class, we’ll explore basic SQL (the most common database language) for business analytics. At the end of the course, students should have a deeper understanding of how databases work, how they fit into the general technology stack, how to connect to databases, and know how to browse and exporting data from databases.

• Department: Decision, Risk and Operations
• Instructor: Mattan Griffel
  Info
• 1.5 points

Intro to Databases for Busines

We don’t think about databases much, right? At least not when they’re working right. But they’re all around us. They’re in every product we use. And when they don’t work (think about the iCloud, LinkedIn, or Ashley Madison data breaches in which hundreds of millions of emails and passwords were exposed) the consequences can be extreme.
Every modern company stores their data in a database (it’s like a really big version of Excel), and if you want to analyze the data, you may be expected to know how to access it yourself. In fact, at many companies are requiring even their business leaders to have an understanding of databases. At the very least, knowing how to set up and interact with databases will improve your ability to GSD (get stuff done), strengthen your understanding of how technology works, and make you less of a pain for developers to work with.
In this class, we’ll explore basic SQL (the most common database language) for business analytics. At the end of the course, students should have a deeper understanding of how databases work, how they fit into the general technology stack, how to connect to databases, and know how to browse and exporting data from databases.

• Department: Decision, Risk and Operations
• Instructor: Mattan Griffel
  Info
• 1.5 points

Antiquity in the Museum: Archaeology, Cu

The seminar introduces graduate students to works of ancient art and architecture held in museum collections. It explores the modern history of their study as antiquities, a category which required a detailed connoisseurship set within a framework of newly arising aesthetic and racial theories and classifications that accompanied imperial archaeological endeavour. The seminar’s focus is on Mesopotamian, Anatolian, Egyptian and Greek antiquities, as ancient works in their original context and as extracted objects that mark an imperial trail. Students will also be introduced to the development of archaeological field methods within the colonial context, and archaeology’s varied forms of visual documentation which became instrumental to imperial knowledge production: architectural and scientific illustrations, excavation images, and archaeological photography, and by the early twentieth century, the introduction of aerial photography as a way of visualizing sites and ruins. Taking ancient works and their display as a starting point, the seminar also explores the ways in which archaeology and the collecting of antiquities were inextricably linked to the technologies and economies of empire and colonialism. Reading and discussions include museum histories and theories of collecting, as well as the history and theories of archaeology and ancient art. Permission of the instructor is required before registration. Please submit a seminar application to the Department of Art History and Archaeology.

• Department: Art History and Archaeology
• Instructor: Zainab Bahrani
  CULPA: 11 Wikipedia Info
• 4 points

Introduction to Randomized Clinical Tria

INTRO TO RAND CLINICAL TR

Students in this course will learn and practice the fundamental methods and concepts of the randomized clinical trial: protocol development, randomization, blindedness, patient recruitment, informed consent, compliance, sample size determination, crossovers, collaborative trials. Each student prepares and submits the protocol for a real or hypothetical clinical trial.

• Department: Biostatistics
• 3 points

Strategic Negotiations

Our days are filled with negotiation and conflict, from everyday disputes to job negotiations, from coalition-building to boardroom bargaining. This course aims to help students improve their skills in two fundamental ways. One is knowledge-oriented: students learn concepts and frameworks for analyzing and preparing for bargaining. A second route is practice-oriented: students engage in a sequence of hands-on activities, practicing and reflecting, building self-awareness, and honing their skills for creating and claiming value.

• Department: Decision, Risk and Operations
• Instructor: Harborne Stuart
  h-index: 10 Info
• 3 points

Clinical Trial Methodology

CLINICAL TRIAL METHODOLOG

Clinical trials are the pilars of clinical research. The main objective of this course is to prepare researchers to design and conduct complex clinical trials that yield valid and reliable results. The course emphasizes on several methodological and practical issues related to the design and analysis of clinical experiments. The course builds on the knowledge and skills gained in the course Randomized Clinical Trial (P8140). The objective of this course is to provide students with working knowledge of certain methodological issues that arise in designing a Clinical Trial. Topics include: Design of small studies (Phase I and II studies), Interim analyses and group sequential methods, Design of survival studies, Multiple outcome measures, Equivalency Trials, Multi-center studies, and trials with multiple outcome measures.

• Department: Biostatistics
• Instructor: Cheng-Shiun Leu
  h-index: 28 Info
• 3 points

Human Population Genetics

HUMAN POPULATION GENETICS

A good grasp of the fundamentals of Population Genetics is crucial for an understanding of any field of human genetics. This is precisely the aim of this course: to provide to students the key elements of Population Genetics with a view to equip them with the right tools to understand the field of genetics in general and to pursue further studies in human genetics. The course uses various evolutionary principles to explain key population genetics concepts.

• Department: Biostatistics
• 3 points

PRACTICE MANAGEMENT

• Department: Dental and Oral Surgery
• 0 points

Python for MBAs

From the ads that track us to the maps that guide us, the twenty-first century runs on code. The business world is no different. Programming has become one of the fastest-growing topics at business schools around the world. This course is an introduction to business uses of Python for MBA students. In this course, well be learning how to write Python code that automates tedious tasks, parses and analyzes large data sets, interact with APIs, and scrapes websites. This might be one of the most useful classes you ever take. Required Course Material Students must have a laptop that they can bring to class - Mac or PC is fine, as long as your operating system is up to date (at least Windows 10 and Mac OS 11). This course does not require a textbook. (Optional Reading: Python for MBAs, Griffel and Guetta) Any required readings will be provided via Canvas. Slides and files will be uploaded to Canvas after each class.
Students will need to complete an introductory Python class (https://courseworks2.columbia.edu/courses/152704) and pass the Basic Python Qualification exam (https://www8.gsb.columbia.edu/courses/python#basic_qual) before the first day of classes.

• Department: Decision, Risk and Operations
• Instructor: Sunny Israni
  Info
• 1.5 points

Analysis of Longitudinal Data

ANALYSIS OF LONGITUDINAL

The course will introduce students to statistical models and mthods for longitudinal data, i.e., repeatedly measured data over time or under different conditions. The topics will include design and sample size calculation, Hotelling's T^2, multivariate analysis of variance, multivariate linear regression (Generalized linear models), models for correlation, unbalanced repeated measurements, Mixed effects models, EM algorithm, methods for non-normally distributed data, Generalized estimating equations, Generalized linear mixed models, and Missing data.

• Department: Biostatistics
• Instructor: Zhonghua Liu
  Info
• 3 points

Vibe Coding

Vibe Coding for Business may be one of the most transformative courses you take in your career. Artificial intelligence is no longer a speculative technology – it is a permanent shift in how organizations operate and create value. Productivity gains from AI are already substantial for many organizations, and are projected to accelerate significantly over the next decade.

A new approach known as vibe coding – enabled by new AI-powdered coding tools such as Cursor, Bolt, and Lovable – has led to a 2-3x increase in the effectiveness of many engineering and product teams. More importantly, vibe coding has opened the door for non-engineers to build working software with little to no coding backgrounds. Suddenly, what used to take a full team and weeks of effort can now be accomplished by a single person and an AI assistant in a matter of hours, drastically reducing the cost and time for product development and experimentation.

In this class, students will learn how to design, build, and deploy lightweight software products using AI coding tools (e.g. Cursor, Lovable, Replit, Bolt, v0). You will practice structured workflows and learn best practices for debugging and iteration, reason through simple systems architectures, evaluate tools critically, and deploy real working applications with speed, reliability, and risk in mind.

This course runs like a product studio: structured tool explorations, weekly build sprints, and a public demo day. It bridges the gap between strategy courses and technical electives (e.g. Python and SQL) by giving business leaders hands-on experience in AI-assisted product creation.

• Department: Decision, Risk and Operations
• Instructor: Mattan Griffel
  Info
• 1.5 points

Management of Patients with Special Need

MGMT OF PTS WITH SPEC NDS

• Department: Dental and Oral Surgery
• Instructor: Marc W Michalowicz
  Info
• 0 points

AI Solutions: From Diagnostics to Deploy

AI solutions are being deployed for an increasing number of problems across industries. At the same time, the ROI on AI deployment has been questioned in certain cases. What drives successful deployment? How does one identify when, and where and what type of AI solutions to deploy? These are exactly the type of questions that many MBA students will face in their careers, is it in the context of managerial roles within large enterprises, as entrepreneurs, or in Consulting, Private Equity, Venture Capital, etc… 

This class aims at helping students sharpen their ``AI acumen’’, and will go through a series of core questions associated with AI deployment and a broad set of business scenarios associated with AI solutions. The first part of the class will focus on the diagnostic step and AI feasibility, while the second part of the course will focus on the deployment of AI solutions. 

● AI Feasibility: the goal is to provide students with practical frameworks and intuition on how to assess efficiently if a business use case in their company is feasible and what steps they need to take to maximize the chance of success. 

● AI Deployment : The goal of this part is to cover practical dimensions of deploying AI solutions, and the tradeoffs they induce. Among others, the course will touch on privacy, cost optimization, accuracy and reliability considerations.

• Department: Decision, Risk and Operations
• 1.5 points

Computing for Investment Analysis

This course is a medium-level introduction to Python and its applications in fundamental analysis, especially estimating the value of companies from fundamental analysis and SEC filings. (If you need a complete introduction, please follow the online "CBS Python Level 1," a prerequisite to this course that I assume all of you have taken. To quote Warren Buffett, we want to automate the work of finding "outstanding companies at sensible prices."

In the first half of the course, we build a quantitative discounted cash flow model in Python to estimate a company's value. We start with a simple one-line formula for the net present value from free cash flow and the average cost of capital. We then add refinements, such as a continuation value, revenue growth predicted from GDP growth, and sensitivity analysis. At the end, we have a tool that takes a stock ticker like "MSFT" and automatically computes a range of intrinsic values for the share price. We apply this tool to US public companies and obtain a list of the most undervalued and overvalued companies.

In the second half of the course, we consider one possible reason for this discrepancy. If a company is under-valued or over-valued, what could the market know that we do not? Is there information that we are missing? Yes, indeed: so far, we have used a company's public disclosures to provide numbers for our quantitative model, but one important piece of information we have not considered is the text. We therefore build a qualitative model from the textual information in a company's public filings. For example, a company may be under federal investigation, and investors are therefore justifiably pessimistic about its future, which could explain a low share price. We therefore apply "text mining" to the risk disclosures in a company's 10-K filing. Our qualitative model identifies new risks within the company and issues a recommendation: "buy" or "sell."

If our quantitative model predicts that a stock is undervalued (the current share price is low relative to fundamentals) and our qualitative model finds no red flags in the risk disclosures, we can recommend the company with reasonable confidence as a "buy." Conversely, if our quantitative model predicts that a company is overvalued and has risk disclosures, we can recommend that the company is a "sell." We then form a long-short portfolio.

Both of these models, quantitative and qualitative, fit into the general 3-part chain below:

- Data (What?): Data gathering,

• Department: Decision, Risk and Operations
• Instructor: Miguel Morin
  Info
• 3 points

COLLOQ IN ATLANTIC HISTORY

This colloquium provides an intensive exploration of the Atlantic World during the early modern era. Readings will attend to the sequence of contact, conquest, and dispossession that enabled the several European empires to gain political and economic power. In this regard, particular attention will be given to the role of commerce and merchant capitalism in the formation of the Atlantic World. The course will focus also, however, on the dynamics of cultural exchange, on the two-way influences that pushed the varied peoples living along the Atlantic to develop new practices, new customs, and new tastes. Creative adaptations in the face of rapid social and cultural change will figure prominently in the readings. Students may expect to give sustained attention the worlds Africans, Amerindians, and Europeans both made together and made apart.

• Department: History
• Instructor: Christopher L Brown
  CULPA: 5 Info
• 4 points

Relational Databases and SQL Programming

RELATIONAL DATABASES AND

In this course, you will learn to design and build relational databases in MySQL and to write and optimize queries using the SQL programming language. Application of skills learned in this course will be geared toward research and data science settings in the healthcare field; however, these skills are transferable to many industries and application areas. You will begin the course examining the pitfalls of using Excel spreadsheets as a data storage tool and then learn how to build properly-designed relational databases to eliminate the issues related to spreadsheets and maintain data integrity when storing and modifying data. You will then learn two aspects of the SQL programming language: 1) the data manipulation language (DML), which allows you to retrieve data from and populate data into database tables (e.g., SELECT, INSERT INTO, DELETE, UPDATE, etc.), and 2) the data definition language (DDL), which allows you to create and modify tables in a database (e.g., CREATE, ALTER, DROP, etc.). You will additionally learn how to optimize SQL queries for best performance, use advanced SQL functions, and utilize SQL within common statistical software programs: R and SAS.

• Department: Biostatistics
• Instructor: Zoe Verzani
  Info
• 3 points

Relational Databases and SQL Programming

RELATIONAL DATABASES AND

In this course, you will learn to design and build relational databases in MySQL and to write and optimize queries using the SQL programming language. Application of skills learned in this course will be geared toward research and data science settings in the healthcare field; however, these skills are transferable to many industries and application areas. You will begin the course examining the pitfalls of using Excel spreadsheets as a data storage tool and then learn how to build properly-designed relational databases to eliminate the issues related to spreadsheets and maintain data integrity when storing and modifying data. You will then learn two aspects of the SQL programming language: 1) the data manipulation language (DML), which allows you to retrieve data from and populate data into database tables (e.g., SELECT, INSERT INTO, DELETE, UPDATE, etc.), and 2) the data definition language (DDL), which allows you to create and modify tables in a database (e.g., CREATE, ALTER, DROP, etc.). You will additionally learn how to optimize SQL queries for best performance, use advanced SQL functions, and utilize SQL within common statistical software programs: R and SAS.

• Department: Biostatistics
• 3 points

Phys & Nutrition Through the Lifecycle

General aspects of normal human growth and development from viewpoints of physical growth, cellular growth and maturation, and adjustments made at birth; the impact of altered nutrition on these processes. Prenatal and postnatal malnutrition, the role of hormones in growth; relationships between nutrition and disease in such areas as anemia, obesity, infection, and carbohydrate absorption.

• Department: Human Nutrition, Institute of
• Instructor: Moneek Madra
  Info
• 3 points

Graduate Seminar in Photo & Related Medi

Grad Sem Photo & Related

Visual Ecologies: Photography, Visual Justice and Environmental Activism. 
Graduate Seminar in Photo & Related Media

This course will explore the intersection of photography, nature, ecology, social and environmental activism, examining art’s role as a catalyst for change. Drawing from critical and historical approaches in photography, landscape studies, architecture, human rights, and interdisciplinary environmental studies, among others, this course offers an exploration of how images can address pressing environmental and social issues while revealing optimism, hope, and collective action in response to our present ecological condition, illuminating the geographies, histories, and ecologies of transformation, liberation, and everyday resistance.

• Department: Visual Arts
• Instructor: Julia Pontes
  Info
• 3 points

NONFICTION THESIS WORKSHOP

• Department: Writing
• Instructor: Chloe Jones
  Info
• Instructor: Jaquira Diaz
  Wikipedia Info
• 9 points